Overview

Dataset statistics

Number of variables20
Number of observations254
Missing cells24
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory39.8 KiB
Average record size in memory160.5 B

Variable types

Categorical7
Numeric12
Text1

Alerts

Avg Bat Strike Rate is highly overall correlated with Run Rate and 2 other fieldsHigh correlation
Format is highly overall correlated with Ground and 2 other fieldsHigh correlation
Fours is highly overall correlated with Highest Score and 1 other fieldsHigh correlation
Ground is highly overall correlated with Format and 1 other fieldsHigh correlation
Highest Individual wicket is highly overall correlated with Wicket TakenHigh correlation
Highest Score is highly overall correlated with Fours and 1 other fieldsHigh correlation
Result is highly overall correlated with TossHigh correlation
Run Rate is highly overall correlated with Avg Bat Strike Rate and 2 other fieldsHigh correlation
Run Scored is highly overall correlated with Fours and 1 other fieldsHigh correlation
Sixes is highly overall correlated with Avg Bat Strike Rate and 1 other fieldsHigh correlation
Toss is highly overall correlated with ResultHigh correlation
Wicket Lost is highly overall correlated with Avg Bat Strike RateHigh correlation
Wicket Taken is highly overall correlated with Highest Individual wicketHigh correlation
Year is highly overall correlated with Format and 1 other fieldsHigh correlation
Result is highly imbalanced (53.6%)Imbalance
Selection is uniformly distributedUniform
Wicket Lost has 3 (1.2%) zerosZeros
Fours has 8 (3.1%) zerosZeros
Sixes has 46 (18.1%) zerosZeros
Wicket Taken has 3 (1.2%) zerosZeros
Highest Individual wicket has 4 (1.6%) zerosZeros

Reproduction

Analysis started2025-12-11 07:10:37.219432
Analysis finished2025-12-11 07:10:59.446070
Duration22.23 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

Team
Categorical

Distinct7
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
Sri Lanka
60 
India
59 
Pakistan
55 
Bangladesh
50 
Afghanistan
14 
Other values (2)
16 

Length

Max length11
Median length9.5
Mean length7.9724409
Min length3

Characters and Unicode

Total characters2025
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPakistan
2nd rowSri Lanka
3rd rowIndia
4th rowSri Lanka
5th rowIndia

Common Values

ValueCountFrequency (%)
Sri Lanka60
23.6%
India59
23.2%
Pakistan55
21.7%
Bangladesh50
19.7%
Afghanistan14
 
5.5%
Hong Kong8
 
3.1%
UAE8
 
3.1%

Length

2025-12-11T12:40:59.588768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T12:40:59.780795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
sri60
18.6%
lanka60
18.6%
india59
18.3%
pakistan55
17.1%
bangladesh50
15.5%
afghanistan14
 
4.3%
hong8
 
2.5%
kong8
 
2.5%
uae8
 
2.5%

Most occurring characters

ValueCountFrequency (%)
a417
20.6%
n268
13.2%
i188
 
9.3%
s119
 
5.9%
k115
 
5.7%
d109
 
5.4%
g80
 
4.0%
t69
 
3.4%
68
 
3.4%
h64
 
3.2%
Other values (15)528
26.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)2025
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a417
20.6%
n268
13.2%
i188
 
9.3%
s119
 
5.9%
k115
 
5.7%
d109
 
5.4%
g80
 
4.0%
t69
 
3.4%
68
 
3.4%
h64
 
3.2%
Other values (15)528
26.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2025
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a417
20.6%
n268
13.2%
i188
 
9.3%
s119
 
5.9%
k115
 
5.7%
d109
 
5.4%
g80
 
4.0%
t69
 
3.4%
68
 
3.4%
h64
 
3.2%
Other values (15)528
26.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2025
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a417
20.6%
n268
13.2%
i188
 
9.3%
s119
 
5.9%
k115
 
5.7%
d109
 
5.4%
g80
 
4.0%
t69
 
3.4%
68
 
3.4%
h64
 
3.2%
Other values (15)528
26.1%

Opponent
Categorical

Distinct7
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
Sri Lanka
60 
India
59 
Pakistan
55 
Bangladesh
50 
Afghanistan
14 
Other values (2)
16 

Length

Max length11
Median length9.5
Mean length7.9724409
Min length3

Characters and Unicode

Total characters2025
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSri Lanka
2nd rowPakistan
3rd rowSri Lanka
4th rowIndia
5th rowPakistan

Common Values

ValueCountFrequency (%)
Sri Lanka60
23.6%
India59
23.2%
Pakistan55
21.7%
Bangladesh50
19.7%
Afghanistan14
 
5.5%
Hong Kong8
 
3.1%
UAE8
 
3.1%

Length

2025-12-11T12:40:59.979797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T12:41:00.119862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
sri60
18.6%
lanka60
18.6%
india59
18.3%
pakistan55
17.1%
bangladesh50
15.5%
afghanistan14
 
4.3%
hong8
 
2.5%
kong8
 
2.5%
uae8
 
2.5%

Most occurring characters

ValueCountFrequency (%)
a417
20.6%
n268
13.2%
i188
 
9.3%
s119
 
5.9%
k115
 
5.7%
d109
 
5.4%
g80
 
4.0%
t69
 
3.4%
68
 
3.4%
h64
 
3.2%
Other values (15)528
26.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)2025
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a417
20.6%
n268
13.2%
i188
 
9.3%
s119
 
5.9%
k115
 
5.7%
d109
 
5.4%
g80
 
4.0%
t69
 
3.4%
68
 
3.4%
h64
 
3.2%
Other values (15)528
26.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2025
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a417
20.6%
n268
13.2%
i188
 
9.3%
s119
 
5.9%
k115
 
5.7%
d109
 
5.4%
g80
 
4.0%
t69
 
3.4%
68
 
3.4%
h64
 
3.2%
Other values (15)528
26.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2025
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a417
20.6%
n268
13.2%
i188
 
9.3%
s119
 
5.9%
k115
 
5.7%
d109
 
5.4%
g80
 
4.0%
t69
 
3.4%
68
 
3.4%
h64
 
3.2%
Other values (15)528
26.1%

Format
Categorical

High correlation 

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
ODI
206 
T20I
48 

Length

Max length4
Median length3
Mean length3.1889764
Min length3

Characters and Unicode

Total characters810
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowODI
2nd rowODI
3rd rowODI
4th rowODI
5th rowODI

Common Values

ValueCountFrequency (%)
ODI206
81.1%
T20I48
 
18.9%

Length

2025-12-11T12:41:00.292266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T12:41:00.400071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
odi206
81.1%
t20i48
 
18.9%

Most occurring characters

ValueCountFrequency (%)
I254
31.4%
O206
25.4%
D206
25.4%
T48
 
5.9%
248
 
5.9%
048
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)810
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I254
31.4%
O206
25.4%
D206
25.4%
T48
 
5.9%
248
 
5.9%
048
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)810
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I254
31.4%
O206
25.4%
D206
25.4%
T48
 
5.9%
248
 
5.9%
048
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)810
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I254
31.4%
O206
25.4%
D206
25.4%
T48
 
5.9%
248
 
5.9%
048
 
5.9%

Ground
Categorical

High correlation 

Distinct18
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
Mirpur
48 
Dubai(DSC)
34 
Sharjah
28 
Dhaka
24 
Dambulla
20 
Other values (13)
100 

Length

Max length12
Median length9
Mean length8.007874
Min length5

Characters and Unicode

Total characters2034
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSharjah
2nd rowSharjah
3rd rowSharjah
4th rowSharjah
5th rowSharjah

Common Values

ValueCountFrequency (%)
Mirpur48
18.9%
Dubai(DSC)34
13.4%
Sharjah28
11.0%
Dhaka24
9.4%
Dambulla20
7.9%
Colombo(RPS)20
7.9%
Karachi20
7.9%
Colombo(SSC)16
 
6.3%
Abu Dhabi10
 
3.9%
Fatullah10
 
3.9%
Other values (8)24
9.4%

Length

2025-12-11T12:41:00.536894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mirpur48
18.2%
dubai(dsc34
12.9%
sharjah28
10.6%
dhaka24
9.1%
dambulla20
7.6%
colombo(rps20
7.6%
karachi20
7.6%
colombo(ssc16
 
6.1%
abu10
 
3.8%
dhabi10
 
3.8%
Other values (9)34
12.9%

Most occurring characters

ValueCountFrequency (%)
a282
13.9%
r158
 
7.8%
h134
 
6.6%
o130
 
6.4%
u126
 
6.2%
D122
 
6.0%
S118
 
5.8%
i114
 
5.6%
b112
 
5.5%
l102
 
5.0%
Other values (23)636
31.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)2034
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a282
13.9%
r158
 
7.8%
h134
 
6.6%
o130
 
6.4%
u126
 
6.2%
D122
 
6.0%
S118
 
5.8%
i114
 
5.6%
b112
 
5.5%
l102
 
5.0%
Other values (23)636
31.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2034
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a282
13.9%
r158
 
7.8%
h134
 
6.6%
o130
 
6.4%
u126
 
6.2%
D122
 
6.0%
S118
 
5.8%
i114
 
5.6%
b112
 
5.5%
l102
 
5.0%
Other values (23)636
31.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2034
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a282
13.9%
r158
 
7.8%
h134
 
6.6%
o130
 
6.4%
u126
 
6.2%
D122
 
6.0%
S118
 
5.8%
i114
 
5.6%
b112
 
5.5%
l102
 
5.0%
Other values (23)636
31.3%

Year
Real number (ℝ)

High correlation 

Distinct16
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2006.9055
Minimum1984
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2025-12-11T12:41:00.680552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1984
5-th percentile1986
Q11997.75
median2008
Q32016
95-th percentile2022
Maximum2022
Range38
Interquartile range (IQR)18.25

Descriptive statistics

Standard deviation11.014495
Coefficient of variation (CV)0.0054882977
Kurtosis-0.83690553
Mean2006.9055
Median Absolute Deviation (MAD)8
Skewness-0.49994105
Sum509754
Variance121.3191
MonotonicityIncreasing
2025-12-11T12:41:00.805032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
202226
10.2%
201826
10.2%
200826
10.2%
200426
10.2%
201422
8.7%
201622
8.7%
198814
 
5.5%
200014
 
5.5%
199514
 
5.5%
199714
 
5.5%
Other values (6)50
19.7%
ValueCountFrequency (%)
19846
 
2.4%
19868
 
3.1%
198814
5.5%
19906
 
2.4%
19912
 
0.8%
199514
5.5%
199714
5.5%
200014
5.5%
200426
10.2%
200826
10.2%
ValueCountFrequency (%)
202226
10.2%
201826
10.2%
201622
8.7%
201422
8.7%
201214
5.5%
201014
5.5%
200826
10.2%
200426
10.2%
200014
5.5%
199714
5.5%

Toss
Categorical

High correlation 

Distinct3
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
Lose
127 
Win
126 
win
 
1

Length

Max length4
Median length3.5
Mean length3.5
Min length3

Characters and Unicode

Total characters889
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st rowLose
2nd rowWin
3rd rowWin
4th rowLose
5th rowWin

Common Values

ValueCountFrequency (%)
Lose127
50.0%
Win126
49.6%
win1
 
0.4%

Length

2025-12-11T12:41:00.950055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T12:41:01.060884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
lose127
50.0%
win127
50.0%

Most occurring characters

ValueCountFrequency (%)
L127
14.3%
o127
14.3%
s127
14.3%
e127
14.3%
i127
14.3%
n127
14.3%
W126
14.2%
w1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)889
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L127
14.3%
o127
14.3%
s127
14.3%
e127
14.3%
i127
14.3%
n127
14.3%
W126
14.2%
w1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)889
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L127
14.3%
o127
14.3%
s127
14.3%
e127
14.3%
i127
14.3%
n127
14.3%
W126
14.2%
w1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)889
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L127
14.3%
o127
14.3%
s127
14.3%
e127
14.3%
i127
14.3%
n127
14.3%
W126
14.2%
w1
 
0.1%

Selection
Categorical

Uniform 

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
Batting
127 
Bowling
127 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters1778
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBatting
2nd rowBowling
3rd rowBowling
4th rowBatting
5th rowBatting

Common Values

ValueCountFrequency (%)
Batting127
50.0%
Bowling127
50.0%

Length

2025-12-11T12:41:01.202754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T12:41:01.690340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
batting127
50.0%
bowling127
50.0%

Most occurring characters

ValueCountFrequency (%)
B254
14.3%
t254
14.3%
i254
14.3%
n254
14.3%
g254
14.3%
a127
7.1%
o127
7.1%
w127
7.1%
l127
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)1778
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B254
14.3%
t254
14.3%
i254
14.3%
n254
14.3%
g254
14.3%
a127
7.1%
o127
7.1%
w127
7.1%
l127
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1778
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B254
14.3%
t254
14.3%
i254
14.3%
n254
14.3%
g254
14.3%
a127
7.1%
o127
7.1%
w127
7.1%
l127
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1778
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B254
14.3%
t254
14.3%
i254
14.3%
n254
14.3%
g254
14.3%
a127
7.1%
o127
7.1%
w127
7.1%
l127
7.1%

Run Scored
Real number (ℝ)

High correlation 

Distinct163
Distinct (%)64.7%
Missing2
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean201.92063
Minimum38
Maximum385
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2025-12-11T12:41:01.851536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum38
5-th percentile102.75
Q1147
median193
Q3253.25
95-th percentile309.45
Maximum385
Range347
Interquartile range (IQR)106.25

Descriptive statistics

Standard deviation67.33186
Coefficient of variation (CV)0.33345705
Kurtosis-0.68254093
Mean201.92063
Median Absolute Deviation (MAD)56
Skewness0.19110736
Sum50884
Variance4533.5793
MonotonicityNot monotonic
2025-12-11T12:41:02.083472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2495
 
2.0%
1785
 
2.0%
1315
 
2.0%
2394
 
1.6%
1294
 
1.6%
1244
 
1.6%
2043
 
1.2%
2713
 
1.2%
1203
 
1.2%
2843
 
1.2%
Other values (153)213
83.9%
ValueCountFrequency (%)
381
0.4%
811
0.4%
822
0.8%
831
0.4%
851
0.4%
871
0.4%
941
0.4%
961
0.4%
971
0.4%
981
0.4%
ValueCountFrequency (%)
3851
0.4%
3741
0.4%
3571
0.4%
3431
0.4%
3321
0.4%
3301
0.4%
3292
0.8%
3261
0.4%
3201
0.4%
3191
0.4%

Wicket Lost
Real number (ℝ)

High correlation  Zeros 

Distinct11
Distinct (%)4.4%
Missing2
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean6.8928571
Minimum0
Maximum10
Zeros3
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2025-12-11T12:41:02.253477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median7
Q310
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8746087
Coefficient of variation (CV)0.41704167
Kurtosis-0.84863215
Mean6.8928571
Median Absolute Deviation (MAD)3
Skewness-0.56129395
Sum1737
Variance8.2633751
MonotonicityNot monotonic
2025-12-11T12:41:02.410584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1071
28.0%
928
 
11.0%
825
 
9.8%
725
 
9.8%
623
 
9.1%
421
 
8.3%
520
 
7.9%
314
 
5.5%
214
 
5.5%
18
 
3.1%
ValueCountFrequency (%)
03
 
1.2%
18
 
3.1%
214
5.5%
314
5.5%
421
8.3%
520
7.9%
623
9.1%
725
9.8%
825
9.8%
928
11.0%
ValueCountFrequency (%)
1071
28.0%
928
 
11.0%
825
 
9.8%
725
 
9.8%
623
 
9.1%
520
 
7.9%
421
 
8.3%
314
 
5.5%
214
 
5.5%
18
 
3.1%

Fours
Real number (ℝ)

High correlation  Zeros 

Distinct36
Distinct (%)14.3%
Missing2
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean15.626984
Minimum0
Maximum41
Zeros8
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2025-12-11T12:41:02.576177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.55
Q110
median15
Q320
95-th percentile30
Maximum41
Range41
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.7549461
Coefficient of variation (CV)0.49625354
Kurtosis0.18201126
Mean15.626984
Median Absolute Deviation (MAD)5
Skewness0.45578583
Sum3938
Variance60.139189
MonotonicityNot monotonic
2025-12-11T12:41:02.742077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
1019
 
7.5%
1516
 
6.3%
1715
 
5.9%
1413
 
5.1%
1613
 
5.1%
912
 
4.7%
1912
 
4.7%
1311
 
4.3%
1111
 
4.3%
2011
 
4.3%
Other values (26)119
46.9%
ValueCountFrequency (%)
08
3.1%
22
 
0.8%
31
 
0.4%
42
 
0.8%
55
 
2.0%
63
 
1.2%
710
3.9%
810
3.9%
912
4.7%
1019
7.5%
ValueCountFrequency (%)
411
 
0.4%
362
 
0.8%
351
 
0.4%
341
 
0.4%
334
1.6%
312
 
0.8%
303
1.2%
295
2.0%
282
 
0.8%
272
 
0.8%

Sixes
Real number (ℝ)

High correlation  Zeros 

Distinct14
Distinct (%)5.6%
Missing2
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean2.9126984
Minimum0
Maximum14
Zeros46
Zeros (%)18.1%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2025-12-11T12:41:02.893284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34.25
95-th percentile8
Maximum14
Range14
Interquartile range (IQR)3.25

Descriptive statistics

Standard deviation2.5964114
Coefficient of variation (CV)0.89141099
Kurtosis1.7522071
Mean2.9126984
Median Absolute Deviation (MAD)2
Skewness1.1787016
Sum734
Variance6.7413521
MonotonicityNot monotonic
2025-12-11T12:41:03.024864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
146
18.1%
046
18.1%
239
15.4%
332
12.6%
426
10.2%
525
9.8%
620
7.9%
86
 
2.4%
73
 
1.2%
93
 
1.2%
Other values (4)6
 
2.4%
ValueCountFrequency (%)
046
18.1%
146
18.1%
239
15.4%
332
12.6%
426
10.2%
525
9.8%
620
7.9%
73
 
1.2%
86
 
2.4%
93
 
1.2%
ValueCountFrequency (%)
141
 
0.4%
122
 
0.8%
111
 
0.4%
102
 
0.8%
93
 
1.2%
86
 
2.4%
73
 
1.2%
620
7.9%
525
9.8%
426
10.2%

Extras
Real number (ℝ)

Distinct33
Distinct (%)13.1%
Missing2
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean13.238095
Minimum0
Maximum38
Zeros2
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2025-12-11T12:41:03.192522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q18
median12
Q318
95-th percentile26.45
Maximum38
Range38
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.6219734
Coefficient of variation (CV)0.57576058
Kurtosis0.97256495
Mean13.238095
Median Absolute Deviation (MAD)5
Skewness0.89889044
Sum3336
Variance58.094479
MonotonicityNot monotonic
2025-12-11T12:41:03.388157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
916
 
6.3%
516
 
6.3%
1316
 
6.3%
716
 
6.3%
814
 
5.5%
1214
 
5.5%
1813
 
5.1%
1413
 
5.1%
1713
 
5.1%
1512
 
4.7%
Other values (23)109
42.9%
ValueCountFrequency (%)
02
 
0.8%
13
 
1.2%
23
 
1.2%
39
3.5%
47
2.8%
516
6.3%
66
 
2.4%
716
6.3%
814
5.5%
916
6.3%
ValueCountFrequency (%)
382
0.8%
373
1.2%
342
0.8%
322
0.8%
293
1.2%
271
 
0.4%
263
1.2%
254
1.6%
243
1.2%
234
1.6%

Run Rate
Real number (ℝ)

High correlation 

Distinct204
Distinct (%)81.0%
Missing2
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean5.3082143
Minimum2.2
Maximum10.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2025-12-11T12:41:03.605859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2.2
5-th percentile3.02
Q14.2275
median5.09
Q36.1
95-th percentile8.695
Maximum10.6
Range8.4
Interquartile range (IQR)1.8725

Descriptive statistics

Standard deviation1.5848226
Coefficient of variation (CV)0.2985604
Kurtosis0.7535938
Mean5.3082143
Median Absolute Deviation (MAD)0.94
Skewness0.76183727
Sum1337.67
Variance2.5116625
MonotonicityNot monotonic
2025-12-11T12:41:03.836451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.454
 
1.6%
3.563
 
1.2%
4.783
 
1.2%
4.643
 
1.2%
4.063
 
1.2%
4.983
 
1.2%
3.53
 
1.2%
5.73
 
1.2%
4.243
 
1.2%
4.743
 
1.2%
Other values (194)221
87.0%
ValueCountFrequency (%)
2.21
0.4%
2.311
0.4%
2.341
0.4%
2.461
0.4%
2.531
0.4%
2.571
0.4%
2.621
0.4%
2.641
0.4%
2.821
0.4%
2.841
0.4%
ValueCountFrequency (%)
10.61
0.4%
10.421
0.4%
9.651
0.4%
9.61
0.4%
9.511
0.4%
9.331
0.4%
9.171
0.4%
9.151
0.4%
9.051
0.4%
8.811
0.4%

Avg Bat Strike Rate
Real number (ℝ)

High correlation 

Distinct248
Distinct (%)98.4%
Missing2
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean76.676667
Minimum24.63
Maximum194.05
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2025-12-11T12:41:04.022896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum24.63
5-th percentile34.244
Q154.7425
median73.155
Q392.8975
95-th percentile135.922
Maximum194.05
Range169.42
Interquartile range (IQR)38.155

Descriptive statistics

Standard deviation30.678228
Coefficient of variation (CV)0.40009861
Kurtosis0.64598305
Mean76.676667
Median Absolute Deviation (MAD)19.28
Skewness0.78513729
Sum19322.52
Variance941.15365
MonotonicityNot monotonic
2025-12-11T12:41:04.224298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43.22
 
0.8%
72.282
 
0.8%
68.112
 
0.8%
93.222
 
0.8%
39.461
 
0.4%
60.481
 
0.4%
68.511
 
0.4%
60.211
 
0.4%
36.791
 
0.4%
46.071
 
0.4%
Other values (238)238
93.7%
(Missing)2
 
0.8%
ValueCountFrequency (%)
24.631
0.4%
25.741
0.4%
28.781
0.4%
28.931
0.4%
28.991
0.4%
29.931
0.4%
30.281
0.4%
31.011
0.4%
31.021
0.4%
31.661
0.4%
ValueCountFrequency (%)
194.051
0.4%
162.361
0.4%
162.111
0.4%
159.031
0.4%
157.121
0.4%
152.981
0.4%
152.321
0.4%
151.541
0.4%
146.481
0.4%
142.441
0.4%

Highest Score
Real number (ℝ)

High correlation 

Distinct98
Distinct (%)38.9%
Missing2
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean69.309524
Minimum8
Maximum183
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2025-12-11T12:41:04.451650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile29.55
Q147
median66.5
Q385
95-th percentile124
Maximum183
Range175
Interquartile range (IQR)38

Descriptive statistics

Standard deviation29.813488
Coefficient of variation (CV)0.43014994
Kurtosis0.13992815
Mean69.309524
Median Absolute Deviation (MAD)19.5
Skewness0.65225092
Sum17466
Variance888.84405
MonotonicityNot monotonic
2025-12-11T12:41:04.683874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
487
 
2.8%
506
 
2.4%
556
 
2.4%
786
 
2.4%
716
 
2.4%
746
 
2.4%
606
 
2.4%
305
 
2.0%
575
 
2.0%
375
 
2.0%
Other values (88)194
76.4%
ValueCountFrequency (%)
81
 
0.4%
201
 
0.4%
222
 
0.8%
231
 
0.4%
252
 
0.8%
264
1.6%
271
 
0.4%
291
 
0.4%
305
2.0%
321
 
0.4%
ValueCountFrequency (%)
1831
 
0.4%
1442
0.8%
1431
 
0.4%
1361
 
0.4%
1351
 
0.4%
1311
 
0.4%
1302
0.8%
1271
 
0.4%
1252
0.8%
1243
1.2%

Wicket Taken
Real number (ℝ)

High correlation  Zeros 

Distinct11
Distinct (%)4.4%
Missing2
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean6.8928571
Minimum0
Maximum10
Zeros3
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2025-12-11T12:41:04.860079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median7
Q310
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8746087
Coefficient of variation (CV)0.41704167
Kurtosis-0.84863215
Mean6.8928571
Median Absolute Deviation (MAD)3
Skewness-0.56129395
Sum1737
Variance8.2633751
MonotonicityNot monotonic
2025-12-11T12:41:04.998609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1071
28.0%
928
 
11.0%
825
 
9.8%
725
 
9.8%
623
 
9.1%
421
 
8.3%
520
 
7.9%
314
 
5.5%
214
 
5.5%
18
 
3.1%
ValueCountFrequency (%)
03
 
1.2%
18
 
3.1%
214
5.5%
314
5.5%
421
8.3%
520
7.9%
623
9.1%
725
9.8%
825
9.8%
928
11.0%
ValueCountFrequency (%)
1071
28.0%
928
 
11.0%
825
 
9.8%
725
 
9.8%
623
 
9.1%
520
 
7.9%
421
 
8.3%
314
 
5.5%
214
 
5.5%
18
 
3.1%

Given Extras
Real number (ℝ)

Distinct33
Distinct (%)13.1%
Missing2
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean13.238095
Minimum0
Maximum38
Zeros2
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2025-12-11T12:41:05.159899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q18
median12
Q318
95-th percentile26.45
Maximum38
Range38
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.6219734
Coefficient of variation (CV)0.57576058
Kurtosis0.97256495
Mean13.238095
Median Absolute Deviation (MAD)5
Skewness0.89889044
Sum3336
Variance58.094479
MonotonicityNot monotonic
2025-12-11T12:41:05.366544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
916
 
6.3%
516
 
6.3%
1316
 
6.3%
716
 
6.3%
814
 
5.5%
1214
 
5.5%
1813
 
5.1%
1413
 
5.1%
1713
 
5.1%
1512
 
4.7%
Other values (23)109
42.9%
ValueCountFrequency (%)
02
 
0.8%
13
 
1.2%
23
 
1.2%
39
3.5%
47
2.8%
516
6.3%
66
 
2.4%
716
6.3%
814
5.5%
916
6.3%
ValueCountFrequency (%)
382
0.8%
373
1.2%
342
0.8%
322
0.8%
293
1.2%
271
 
0.4%
263
1.2%
254
1.6%
243
1.2%
234
1.6%

Highest Individual wicket
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)2.8%
Missing2
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean2.5634921
Minimum0
Maximum6
Zeros4
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2025-12-11T12:41:05.517611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile4.45
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1004963
Coefficient of variation (CV)0.42929578
Kurtosis0.0066387497
Mean2.5634921
Median Absolute Deviation (MAD)1
Skewness0.28032486
Sum646
Variance1.2110921
MonotonicityNot monotonic
2025-12-11T12:41:05.635535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
284
33.1%
384
33.1%
136
14.2%
431
 
12.2%
512
 
4.7%
04
 
1.6%
61
 
0.4%
(Missing)2
 
0.8%
ValueCountFrequency (%)
04
 
1.6%
136
14.2%
284
33.1%
384
33.1%
431
 
12.2%
512
 
4.7%
61
 
0.4%
ValueCountFrequency (%)
61
 
0.4%
512
 
4.7%
431
 
12.2%
384
33.1%
284
33.1%
136
14.2%
04
 
1.6%
Distinct78
Distinct (%)31.0%
Missing2
Missing (%)0.8%
Memory size2.1 KiB
2025-12-11T12:41:06.301401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length26
Median length18
Mean length13.746032
Min length8

Characters and Unicode

Total characters3464
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRoy Dias
2nd rowRoy Dias
3rd rowSurinder Khanna
4th rowSurinder Khanna
5th rowSurinder Khanna
ValueCountFrequency (%)
mohammad20
 
3.9%
khan14
 
2.7%
virat12
 
2.3%
kohli12
 
2.3%
sanath10
 
1.9%
malik10
 
1.9%
kumar10
 
1.9%
jayasuriya10
 
1.9%
shoaib10
 
1.9%
sangakkara8
 
1.5%
Other values (131)402
77.6%
2025-12-11T12:41:07.289578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a634
18.3%
266
 
7.7%
h242
 
7.0%
i232
 
6.7%
n202
 
5.8%
r194
 
5.6%
u152
 
4.4%
d124
 
3.6%
m114
 
3.3%
S110
 
3.2%
Other values (39)1194
34.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)3464
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a634
18.3%
266
 
7.7%
h242
 
7.0%
i232
 
6.7%
n202
 
5.8%
r194
 
5.6%
u152
 
4.4%
d124
 
3.6%
m114
 
3.3%
S110
 
3.2%
Other values (39)1194
34.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3464
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a634
18.3%
266
 
7.7%
h242
 
7.0%
i232
 
6.7%
n202
 
5.8%
r194
 
5.6%
u152
 
4.4%
d124
 
3.6%
m114
 
3.3%
S110
 
3.2%
Other values (39)1194
34.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3464
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a634
18.3%
266
 
7.7%
h242
 
7.0%
i232
 
6.7%
n202
 
5.8%
r194
 
5.6%
u152
 
4.4%
d124
 
3.6%
m114
 
3.3%
S110
 
3.2%
Other values (39)1194
34.5%

Result
Categorical

High correlation  Imbalance 

Distinct6
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
Lose
124 
Win
123 
No Result
 
4
win
 
1
Win D/L
 
1

Length

Max length9
Median length8
Mean length3.6259843
Min length3

Characters and Unicode

Total characters921
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)1.2%

Sample

1st rowLose
2nd rowWin
3rd rowWin
4th rowLose
5th rowWin

Common Values

ValueCountFrequency (%)
Lose124
48.8%
Win123
48.4%
No Result4
 
1.6%
win1
 
0.4%
Win D/L1
 
0.4%
Lose D/L1
 
0.4%

Length

2025-12-11T12:41:07.431695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T12:41:07.532932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
lose125
48.1%
win125
48.1%
no4
 
1.5%
result4
 
1.5%
d/l2
 
0.8%

Most occurring characters

ValueCountFrequency (%)
o129
14.0%
s129
14.0%
e129
14.0%
L127
13.8%
i125
13.6%
n125
13.6%
W124
13.5%
8
 
0.9%
N4
 
0.4%
R4
 
0.4%
Other values (6)17
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)921
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o129
14.0%
s129
14.0%
e129
14.0%
L127
13.8%
i125
13.6%
n125
13.6%
W124
13.5%
8
 
0.9%
N4
 
0.4%
R4
 
0.4%
Other values (6)17
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)921
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o129
14.0%
s129
14.0%
e129
14.0%
L127
13.8%
i125
13.6%
n125
13.6%
W124
13.5%
8
 
0.9%
N4
 
0.4%
R4
 
0.4%
Other values (6)17
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)921
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o129
14.0%
s129
14.0%
e129
14.0%
L127
13.8%
i125
13.6%
n125
13.6%
W124
13.5%
8
 
0.9%
N4
 
0.4%
R4
 
0.4%
Other values (6)17
 
1.8%

Interactions

2025-12-11T12:40:56.573374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:38.590170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:40.301063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:42.208354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:43.775901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:45.262376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:46.853670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:48.465900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:49.963134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:51.878024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:53.475177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:55.105363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:56.743499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:38.765238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:40.455834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:42.363570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:43.909032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:45.397230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:46.993680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:48.581439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:50.105429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:52.016530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:53.629991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:55.247793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:56.874565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:38.920682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:40.589275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:42.499577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:44.037928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:45.570508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:47.120122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:48.709409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:50.241357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:52.154167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:53.774501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:55.387681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:57.037115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:39.042678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:40.731402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:42.614115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:44.158822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:45.703593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:47.267628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:48.821181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:50.368009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:52.277228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:53.900758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:55.530222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:57.189832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:39.147177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:40.862814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:42.738149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:44.280564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:45.840015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:47.407372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:48.946634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:50.798389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:52.392108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:54.035942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:55.642972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:57.345636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:39.269960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:41.004328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:42.856240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:44.413824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:45.963657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:47.545081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:49.077766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:50.942688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-11T12:40:54.160902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:55.763434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:57.520304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:39.415612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:41.119239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:42.980976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:44.540689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:46.083845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:47.679309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:49.191015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:51.087487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:52.684028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:54.322585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:55.871681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:57.654210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:39.552097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:41.249669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:43.116252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:44.645471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:46.210759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:47.800532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-11T12:40:51.225465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:52.808474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:54.455073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:55.975498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:57.806379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:39.701252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:41.406973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:43.258254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:44.752063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:46.348877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:47.924762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:49.445040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:51.330805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:52.941835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:54.572275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:56.087425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:57.980368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:39.860322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:41.576058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:43.406202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:44.875566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:46.484349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:48.082386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:49.595393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:51.488443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:53.075832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:54.716516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:56.196778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:58.132083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:39.990878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:41.738084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:43.537759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:44.990613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:46.611618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:48.206601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:49.698783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:51.603028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:53.195431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:54.813753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:56.299492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:58.296491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:40.127519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:41.900485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:43.672958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:45.116815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:46.723787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:48.329217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:49.828974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:51.760076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:53.338610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:54.957812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T12:40:56.421010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-12-11T12:41:07.696599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Avg Bat Strike RateExtrasFormatFoursGiven ExtrasGroundHighest Individual wicketHighest ScoreOpponentResultRun RateRun ScoredSelectionSixesTeamTossWicket LostWicket TakenYear
Avg Bat Strike Rate1.000-0.2180.4350.470-0.0580.0000.1840.3810.1330.1730.8750.4200.0000.6000.1820.000-0.5720.1800.357
Extras-0.2181.0000.3780.0570.3540.199-0.0570.1000.0850.000-0.2250.2610.000-0.2290.0590.0000.1360.017-0.451
Format0.4350.3781.0000.2940.3780.5130.0000.2700.1080.0000.6830.4490.0000.2860.1080.0000.1580.1580.813
Fours0.4700.0570.2941.0000.1990.2620.1620.6470.0000.1570.4390.7680.0000.3360.0780.090-0.1870.2050.123
Given Extras-0.0580.3540.3780.1991.0000.1990.0900.1950.0590.000-0.1450.2650.000-0.1750.0850.0000.0170.136-0.451
Ground0.0000.1990.5130.2620.1991.0000.0000.1350.0930.2370.1920.2200.0000.0000.0930.4290.0120.0120.698
Highest Individual wicket0.184-0.0570.0000.1620.0900.0001.0000.1920.0600.1510.1460.1660.2140.0640.1240.000-0.1710.7310.058
Highest Score0.3810.1000.2700.6470.1950.1350.1921.0000.0710.1210.3940.7740.1320.4080.1580.119-0.3280.192-0.014
Opponent0.1330.0850.1080.0000.0590.0930.0600.0711.0000.1780.1080.0910.2350.1410.1800.0000.1820.1650.000
Result0.1730.0000.0000.1570.0000.2370.1510.1210.1781.0000.1430.0810.0000.1300.1770.7070.2600.2500.056
Run Rate0.875-0.2250.6830.439-0.1450.1920.1460.3940.1080.1431.0000.3350.0000.6450.1970.000-0.5000.1370.476
Run Scored0.4200.2610.4490.7680.2650.2200.1660.7740.0910.0810.3351.0000.2210.3910.1480.000-0.1220.194-0.070
Selection0.0000.0000.0000.0000.0000.0000.2140.1320.2350.0000.0000.2211.0000.0000.2350.0000.3800.3800.000
Sixes0.600-0.2290.2860.336-0.1750.0000.0640.4080.1410.1300.6450.3910.0001.0000.1230.000-0.2510.0480.491
Team0.1820.0590.1080.0780.0850.0930.1240.1580.1800.1770.1970.1480.2350.1231.0000.0000.1650.1820.000
Toss0.0000.0000.0000.0900.0000.4290.0000.1190.0000.7070.0000.0000.0000.0000.0001.0000.1490.0820.000
Wicket Lost-0.5720.1360.158-0.1870.0170.012-0.171-0.3280.1820.260-0.500-0.1220.380-0.2510.1650.1491.000-0.2350.001
Wicket Taken0.1800.0170.1580.2050.1360.0120.7310.1920.1650.2500.1370.1940.3800.0480.1820.082-0.2351.0000.001
Year0.357-0.4510.8130.123-0.4510.6980.058-0.0140.0000.0560.476-0.0700.0000.4910.0000.0000.0010.0011.000

Missing values

2025-12-11T12:40:58.546153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-11T12:40:58.807372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-12-11T12:40:59.128813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

TeamOpponentFormatGroundYearTossSelectionRun ScoredWicket LostFoursSixesExtrasRun RateAvg Bat Strike RateHighest ScoreWicket TakenGiven ExtrasHighest Individual wicketPlayer Of The MatchResult
0PakistanSri LankaODISharjah1984LoseBatting187.09.09.03.021.04.0652.0447.05.026.02.0Roy DiasLose
1Sri LankaPakistanODISharjah1984WinBowling190.05.011.01.026.04.3668.5157.09.021.03.0Roy DiasWin
2IndiaSri LankaODISharjah1984WinBowling97.00.09.00.014.04.4760.4851.010.08.03.0Surinder KhannaWin
3Sri LankaIndiaODISharjah1984LoseBatting96.010.07.00.08.02.3425.7438.00.014.00.0Surinder KhannaLose
4IndiaPakistanODISharjah1984WinBatting188.04.013.03.017.04.0860.2156.010.05.03.0Surinder KhannaWin
5PakistanIndiaODISharjah1984LoseBowling134.010.05.00.05.03.3739.4635.04.017.01.0Surinder KhannaLose
6Sri LankaPakistanODIColombo(PSS)1986WinBowling116.010.010.00.014.03.4237.8734.010.015.03.0Mohsin KhanLose
7PakistanSri LankaODIColombo(PSS)1986LoseBatting197.010.014.03.015.04.3765.1439.010.014.03.0Mohsin KhanWin
8BangladeshPakistanODIMoratuwa1986LoseBatting94.010.00.00.09.02.6424.6337.03.05.02.0Wasim AkramLose
9PakistanBangladeshODIMoratuwa1986WinBowling98.03.04.00.05.03.0436.7947.010.09.04.0Wasim AkramWin
TeamOpponentFormatGroundYearTossSelectionRun ScoredWicket LostFoursSixesExtrasRun RateAvg Bat Strike RateHighest ScoreWicket TakenGiven ExtrasHighest Individual wicketPlayer Of The MatchResult
244IndiaSri LankaT20IDubai(DSC)2022LoseBatting173.08.010.07.08.08.65102.9472.04.06.03.0Dasun ShanakaLose
245Sri LankaIndiaT20IDubai(DSC)2022WinBowling174.04.012.08.06.08.77106.5457.08.08.03.0Dasun ShanakaWin
246AfghanistanPakistanT20ISharjah2022LoseBatting129.06.010.05.03.06.4593.3135.09.05.03.0Shadab KhanLose
247PakistanAfghanistanT20ISharjah2022WinBowling131.09.06.08.05.06.77102.5236.06.03.02.0Shadab KhanWin
248AfghanistanIndiaT20IDubai(DSC)2022WinBowling111.08.09.03.03.05.5551.2564.02.02.02.0Virat KohliLose
249IndiaAfghanistanT20IDubai(DSC)2022LoseBatting212.02.021.09.02.010.60194.05122.08.03.05.0Virat KohliWin
250PakistanSri LankaT20IDubai(DSC)2022LoseBatting121.010.04.03.017.06.3158.7830.05.05.02.0Wanindu Hasaranga de SilvaLose
251Sri LankaPakistanT20IDubai(DSC)2022WinBowling124.05.09.05.05.07.29111.5055.010.017.03.0Wanindu Hasaranga de SilvaWin
252PakistanSri LankaT20IDubai(DSC)2022WinBowling147.010.09.04.014.07.3599.3855.06.010.03.0Bhanuka RajapaksaLose
253Sri LankaPakistanT20IDubai(DSC)2022LoseBatting170.06.016.05.010.08.5090.8671.010.014.04.0Bhanuka RajapaksaWin